abstract = "A conceptual clustering framework is presented which
can be applied to multi-relational knowledge bases
storing resource annotations expressed in the standard
languages for the Semantic Web. The framework adopts an
effective and language-independent family of
semi-distance measures defined for the space of
individual resources. These measures are based on a
finite number of dimensions corresponding to a
committee of discriminating features represented by
concept descriptions. The clustering algorithm
expresses the possible clusterings in terms of strings
of central elements (medoids, w.r.t. the given metric)
of variable length. The method performs a stochastic
search in the space of possible clusterings, exploiting
a technique based on genetic programming. Besides, the
number of clusters is not necessarily required as a
parameter: a natural number of clusters is autonomously
determined, since the search spans a space of strings
of different length. An experimentation with real
ontologies proves the feasibility of the clustering
method and its effectiveness in terms of standard
validity indices. The framework is completed by a
successive phase, where a newly constructed intensional
definition, expressed in the adopted concept language,
can be assigned to each cluster. Finally, two possible
extensions are proposed. One allows the induction of
hierarchies of clusters. The other applies clustering
to concept drift and novelty detection in the context
of ontologies.",